We propose a novel transformer-style architecture called Global-Local Filter
Network (GLFNet) for medical image segmentation and demonstrate its
state-of-the-art performance. We replace the self-attention mechanism with a
combination of global-local filter blocks to optimize model efficiency. The
global filters extract features from the whole feature map whereas the local
filters are being adaptively created as 4x4 patches of the same feature map and
add restricted scale information. In particular, the feature extraction takes
place in the frequency domain rather than the commonly used spatial (image)
domain to facilitate faster computations. The fusion of information from both
spatial and frequency spaces creates an efficient model with regards to
complexity, required data and performance. We test GLFNet on three benchmark
datasets achieving state-of-the-art performance on all of them while being
almost twice as efficient in terms of GFLOP operations.

我们提出了一种名为全局 - 局部滤波网络（GLFNet）的新型 Transformer 风格架构，用于医学图像分割，证明了其具有最先进的性能。我们用全局 - 局部滤波块取代了自注意机制，以优化模型效率。全局滤波器从整个特征图中提取特征，而局部滤波器以适应性方式作为相同特征图的 4x4 补丁生成并添加受限的尺度信息。特征提取发生在频率域而不是常用的空间（图像）域，以实现更快的计算。来自空间和频率空间的信息融合创建了一个在复杂性、所需数据和性能方面效率高的模型。我们在三个基准数据集上测试了 GLFNet，在所有数据集上都实现了最先进的性能，而在 GFLOP 操作方面几乎提高了一倍的效率。

GLFNET：用于高效医学图像分割的全局 - 局部（频率）滤波网络

GLFNET: Global-Local (frequency) Filter Networks for efficient medical  image segmentation

Video action anticipation aims to predict future action categories from
observed frames. Current state-of-the-art approaches mainly resort to recurrent
neural networks to encode history information into hidden states, and predict
future actions from the hidden representations. It is well known that the
recurrent pipeline is inefficient in capturing long-term information which may
limit its performance in predication task. To address this problem, this paper
proposes a simple yet efficient Temporal Transformer with Progressive
Prediction (TTPP) framework, which repurposes a Transformer-style architecture
to aggregate observed features, and then leverages a light-weight network to
progressively predict future features and actions. Specifically, predicted
features along with predicted probabilities are accumulated into the inputs of
subsequent prediction. We evaluate our approach on three action datasets,
namely TVSeries, THUMOS-14, and TV-Human-Interaction. Additionally we also
conduct a comprehensive study for several popular aggregation and prediction
strategies. Extensive results show that TTPP not only outperforms the
state-of-the-art methods but also more efficient.

本文提出一个名为 TTPP 的框架，使用 Transformer 风格的架构，通过汇总观察到的特征，并利用轻量的网络逐步预测未来的特征和行动，来解决当前流行的基于循环神经网络的视频行动预测模型中无法有效捕获长期信息的缺点，在三个数据集上的广泛结果表明，TTPP 不仅胜过现有技术方法，而且更有效。

TTPP: 时间变形器及渐进式预测，用于高效行动预测

TTPP: Temporal Transformer with Progressive Prediction for Efficient  Action Anticipation

We introduce the Action Transformer model for recognizing and localizing
human actions in video clips. We repurpose a Transformer-style architecture to
aggregate features from the spatiotemporal context around the person whose
actions we are trying to classify. We show that by using high-resolution,
person-specific, class-agnostic queries, the model spontaneously learns to
track individual people and to pick up on semantic context from the actions of
others. Additionally its attention mechanism learns to emphasize hands and
faces, which are often crucial to discriminate an action - all without explicit
supervision other than boxes and class labels. We train and test our Action
Transformer network on the Atomic Visual Actions (AVA) dataset, outperforming
the state-of-the-art by a significant margin using only raw RGB frames as
input.

本文介绍了行动转换器模型用于识别和定位视频剪辑中的人类动作，使用 Transformer 风格的架构聚合人物周围的时空背景特征，通过高分辨率、个性化、类别不可知的查询，该模型自动学习跟踪个人并从他人的动作中获取语义上下文